Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park 2006, South Africa.
Department of Electrical Engineering, University of South Africa, Florida 1710, South Africa.
Comput Intell Neurosci. 2022 Aug 23;2022:2142935. doi: 10.1155/2022/2142935. eCollection 2022.
In this article, we present the recognition of nonintrusive disaggregated appliance signals through a reduced dataset computer vision deep learning approach. Deep learning data requirements are costly in terms of acquisition time, storage memory requirements, computation time, and dynamic memory usage. We develop our recognition strategy on Siamese and prototypical reduced data few-shot classification algorithms. Siamese networks address the 1-shot recognition well. Appliance activation periods vary considerably, and this can result in imbalance in the number of appliance-specific generated signal images. Prototypical networks address the problem of data imbalance in training. By first carrying out a similarity test on the entire dataset, we establish the quality of our data before input into the deep learning algorithms. The results give acceptable performance and show the promise of few-shot learning in recognizing appliances in the nonintrusive load-monitoring scheme for very limited data samples.
在本文中，我们通过一种减少数据集的计算机视觉深度学习方法来实现对非侵入式离散设备信号的识别。深度学习数据在采集时间、存储内存需求、计算时间和动态内存使用方面的要求都很高。我们在 Siamese 和原型减少数据少镜头分类算法上开发了我们的识别策略。Siamese 网络很好地解决了 1 镜头识别问题。设备激活周期差异很大，这可能导致特定设备生成信号图像的数量不平衡。原型网络解决了训练中数据不平衡的问题。通过首先对整个数据集进行相似性测试，我们在将数据输入深度学习算法之前确定其质量。结果表明，在非常有限的数据样本的非侵入式负载监测方案中，少镜头学习在识别设备方面具有良好的性能和应用前景。
